Mapping Fiber and Yield QTLs with Main, Epistatic, and QTL × Environment Interaction Effects in Recombinant Inbred Lines of Upland Cotton

Shen, Xinlian; Zhang, Tianzhen; Guo, Wenshan; Zhu, Xiefei; Zhang, Xiaoyang

doi:10.2135/cropsci2005.0056

Cited by 91 publications

(66 citation statements)

References 30 publications

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“…Earlier, we identified several loci controlling SSC in soybean (Dhungana et al 2017) and observed that majority of the QTL for SSC were found to be significantly influenced by the growing environment. Such effects have been observed for other complex traits like forage quality (Asekova et al 2016), isoflavone content (Primomo et al 2005), seed weight , seed protein (Panthee et al 2005), fatty acid composition, oil and protein contents (Rennie and Tanner 1989;Dornbos and Mullen 1992;Primomo et al 2002;Fehr et al 2003;Hou et al 2006;Oliva et al 2006;Dhakal et al 2013;Asekova et al 2014), and yield (Rao et al 2002;Shen et al 2006) in soybean.…”

Section: Introductionmentioning

confidence: 93%

Environmental Stability and Correlation of Soybean Seed Starch with Protein and Oil Contents

Dhungana

Kulkarni

Kim

et al. 2017

Plant Breed. Biotech.

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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT Seed starch content (SSC) is a decisive factor influencing soy food quality. Variation in SSC affects the composition of major components, oil, and protein in soybean seeds. Therefore, understanding G × E interaction of SSC is important to produce soybeans with stable SSC. In the present study, G × E interactions of 17 soybean genotypes having different SSC (0.24-1.48%) and correlation of SSC with crude protein (CP) and crude fat (CF) were investigated. The genotypes were evaluated for SSC and other traits at two planting dates across three locations over two years (2015 and 2016). The genotype × year, genotype × location, and genotype × year × location interactions were found to be significant (P ≤ 0.001) for SSC, CP, and CF. The average SSC content was found to be higher in 2015 than in 2016. Late planted soybeans contained higher SSC than the early planting soybeans. The SSC was negatively affected by the average daily mean and minimum temperatures and cloudiness during the pod-filling stage. Based on the mean rank, IT189276 (1.39%) was observed to be the most stable genotype among the high starch containing soybeans. Significant (P ≤ 0.0001) negative correlations were found between SSC and CP as well as CP and CF contents. However, a significant (P ≤ 0.05) positive correlation was observed between SSC and CF content. Results of this study showed that SSC affects the seed protein and oil contents and is significantly influenced by the growing environments.

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Section: Introductionmentioning

confidence: 93%

Environmental Stability and Correlation of Soybean Seed Starch with Protein and Oil Contents

Dhungana

Kulkarni

Kim

et al. 2017

Plant Breed. Biotech.

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“…Cottonseed protein cake and oil are secondary products and have been drawing a large interest as animal feed (Arieli 1998), human consumption (O'Brien and Wakelyn 2005), and biodiesel feedstock (Royon et al 2007). With the advent of molecular marker technology, it became possible to construct saturated genetic maps (Rong et al 2004;Guo et al 2007) and to locate QTL for lint yield (Shen et al 2006b;He et al 2007), fiber quality (Paterson et al 2003;Lacape et al 2005;Park et al 2005), seed traits , and response to biotic or abiotic stress (Wright et al 1998;Saranga et al 2004;Shen et al 2006a) to linkage groups or chromosomes in cotton. However, limited information is known about the direct association or interaction among specific genes and the variations of traits of interest such as cotton fiber (Ruan et al 2003;Arpat et al 2004;Lee et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Use of fiber and fuzz mutants to detect QTL for yield components, seed, and fiber traits of upland cotton

Jenkins

et al. 2009

Euphytica

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This research detected QTL or molecular markers associated with yield, fiber, and seed traits within multiple fuzz and fiber loci genetic backgrounds. Two F 2 populations from crosses of MD17, a fuzzless-lintless line containing three fuzzless loci, N 1 , n 2 and a postulated n 3 , with line 181, fuzzlesslinted and with FM966, a fuzzy-linted cultivar, were used. QTL explaining 68.3 (population with FM966) to 87.1% (population with 181) of the phenotypic variation for lint percentage and 62.8% (population with 181) for lint index were detected in the vicinity of BNL3482-138 on chromosome 26. Single marker regression analyses indicated STV79-108, on the long arm of chromosome 12 had significant association with lint percentage (R 2 26.7%), lint index (R 2 30.6%), embryo protein percentage (R 2 15.4%) and micronaire (R 2 20.0%). Two-locus epistatic interactions were also observed. Results from this research will facilitate further understanding the complex mechanisms of yield, fiber, and seed traits of cotton.

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“…However, indirect selection in breeding is also difficult in breeding practice because of neglect in the correlation among non-target traits and the lack of an effective method with which to unify the same genetic effects of different traits (Galanopoulou-Sendouca and Roupakias 1999; Mccarty et al 2008). Modern molecular biology and quantitative genetics reveal that QTL has various effects, such as additive and dominance effects, among others (Shen et al 2006;Jiao et al 2010). Its complex correlativity makes the genetic relationship among characters much more difficult to Table 8 Decision-making coefficients (R i ) of the genetic components of other characters to fiber length, strength, and micronaire individual and order from maximum to minimum for R select than non-target traits.…”

Section: Discussionmentioning

confidence: 99%

Analysis of decision-making coefficients of three main fiber quality traits for upland cotton (Gossypium hirsutum L.)

Mei

Fan

et al. 2013

Euphytica

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The analysis of genetic correlations between fiber length (Len), strength (Str), micronaire, and 12 other traits was conducted using the additive (A)-dominance (D) genetic model, which considers genotype 9 environment interaction effects, in intraspecific upland cotton (Gossypium hirsutum L.) hybrids to effectively improve the quality of cotton cultivars in high planting density cases. Decisionmaking coefficients were computed based on the genetic correlation and path analysis of three fiber quality traits. The decision-making coefficient analysis of three fiber traits in cross breeding was beneficial for the improvement of Len by increasing the additive effects of Str and length of boll (LB) and decreasing lint percentage (LP), boll number of the top three fruitbearing branches. The analysis was also beneficial for the improvement of Str and fiber fineness by increasing or decreasing the additive effects of other traits. Utilizing heterosis in hybrids was beneficial to the heterosis of Len by selecting the high dominance effects of number of nodes of the 1st fruit-bearing branch and LB and decreasing the dominance effects of diameter of boll (DB) and LP and for improving Str by increasing the dominance effects of DB and decreasing the dominance effects of number of fruitbearing branches and number of nodes of the main stem (NNMS). Utilizing heterosis was also beneficial for improving fineness by increasing the dominance effects of LB, Str, and lint yield and decreasing the dominance effects of NNMS and Len.

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Mapping Fiber and Yield QTLs with Main, Epistatic, and QTL × Environment Interaction Effects in Recombinant Inbred Lines of Upland Cotton

Cited by 91 publications

References 30 publications

Environmental Stability and Correlation of Soybean Seed Starch with Protein and Oil Contents

Environmental Stability and Correlation of Soybean Seed Starch with Protein and Oil Contents

Use of fiber and fuzz mutants to detect QTL for yield components, seed, and fiber traits of upland cotton

Analysis of decision-making coefficients of three main fiber quality traits for upland cotton (Gossypium hirsutum L.)

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